Abstract
By assessing the cognitive capital, neuropsychological evaluation (NPE) plays a vital role in the perioperative workup of patients with refractory focal epilepsy. In this retrospective study, we used cutting-edge statistical approaches to examine a group of 47 patients with refractory temporal lobe epilepsy (TLE), who underwent standard anterior temporal lobectomy (ATL). Our objective was to determine whether NPE may represent a robust predictor of the postoperative status, two years after surgery. Specifically, based on pre- and postsurgical neuropsychological data, we estimated the sensitivity of cognitive indicators to predict and to disentangle phenotypes associated with more or less favorable outcomes. Engel (ENG) scores were used to assess clinical outcome, and picture naming (NAM) performance to estimate naming status. Two methods were applied: (a) machine learning (ML) to explore cognitive sensitivity to postoperative outcomes; and (b) graph theory (GT) to assess network properties reflecting favorable vs. less favorable phenotypes after surgery. Specific neuropsychological indices assessing language, memory, and executive functions can globally predict outcomes. Interestingly, preoperative cognitive networks associated with poor postsurgical outcome already exhibit an atypical, highly modular and less densely interconnected configuration. We provide statistical and clinical tools to anticipate the condition after surgery and achieve a more personalized clinical management. Our results also shed light on possible mechanisms put in place for cognitive adaptation after acute injury of central nervous system in relation with surgery.
Highlights
The central role of neuropsychology in epilepsy has been historically rooted, notably in the context of neurosurgery for refractory epilepsy
We developed several Machine Learning workflows trying each time three different algorithms belonging to a different family: (a) a classical Support Vector Machine (SVM) algorithm [49] with a Radial Basis Function (RBF), (b) XGBoost algorithm [50] and (c) a logistic regression with penalty L2
The prediction of ENG based on the 4 selected features is further improved: balanced accuracy (BAcc) = 84.4% ± 3.7%, with a remarkable level of prediction for the class corresponding to a worse postoperative clinical prognosis in particular (ENGÀ = 89.9%; Fig. 2 Panel A)
Summary
The central role of neuropsychology in epilepsy has been historically rooted, notably in the context of neurosurgery for refractory epilepsy. Major discoveries such as Penfield’s homunculus [1], the functional specialization of the hippocampal–temporal lobe complex in memory [2,3], or the functional lateralization of certain cognitive functions as observed via the Wada test [4] or surgical callosotomy and ‘‘split brain” patients [5] have been documented in this particular context [6]. They are already detected by the time of epilepsy’s onset. It is estimated that 70% of adult patients with newly diagnosed epilepsy have at least one proven cognitive deficit well before the introduction of the medication [14]
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